Predictive Analysis on Apodized FBG for Quasi-Distributed Temperature-Strain Sensing

Mandal, Himadri Nirjhar; Sidhishwari, Soumya

doi:10.1109/spcom55316.2022.9840764

Cited by 6 publications

(2 citation statements)

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“…Although, the optimization of the reflectivity as well as the bandwidth can be performed through varying the extent of the grating. For wavelength division multiplexing (WDM) based sensing network, FBGs with high reflectivity is very much essential for enhancing the signal to noise ratio [8]. In quasi-distributed approach, various FBGs are incorporated on independent portion of an optical fiber on the basis of WDM comprises of several Bragg wavelengths to determine the situations of any civil infrastructures like the buildings as well as the bridges [9].…”

Section: Introductionmentioning

confidence: 99%

Optimization and analysis of apodized fiber Bragg grating properties for quasi-distributed sensing

Mandal,

Sidhishwari

2024

Phys. Scr.

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An apodized fiber Bragg grating (FBG) is introduced with a proposed apodization function for the effective quasi-distributed sensing estimation of the temperature and the strain. FBG features such as reflectivity, side lobes, and bandwidth have been optimized for the designed apodized grating to upgrade the effectiveness of FBG for properly measuring the variations in the Bragg wavelength. Based upon the simulation, a comparative analytical study on FBG properties with different apodization function profiles has been demonstrated to achieve the optimum profile with high reflectivity, narrow bandwidth and minimal range of side lobes for quasi-distributed sensing estimation of parameters. A strong linearity has been noted for the sensitivity of designed FBG with different apodization profiles for the temperature and the strain estimation subsequently. It has been reported that the obtained sensitivity of measurands for the FBG with proposed apodization profile are higher as compared with the other profiles. The wavelength division multiplexing (WDM) based quasi-distributed network of four optimized FBGs have been implemented for different apodization profiles to illustrate the impact of apodization to prevent overlapping between neighbouring FBG spectrums in the sensing network with spatial resolution of 2 nm. The maximal detectable temperature/strain sensitivity estimation of 153 °C / 1439 μϵ have been obtained for the proposed apodized FBG along with minimal detectable temperature/strain ranges of -102 °C / -1345 μϵ in the quasi-distributed network. The achieved ranges with optimum resolution can be implemented effectively in quasi-distributed based sensing application for condition observation of civil infrastructures in any complex circumstances.

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Section: Introductionmentioning

confidence: 99%

Optimization and analysis of apodized fiber Bragg grating properties for quasi-distributed sensing

Mandal,

Sidhishwari

2024

Phys. Scr.

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“…The presence of side lobes in a high range generally affects the measurement of the central wavelength in the reflectivity spectrum, particularly in the case of quasi-distributed sensing [4]. The spatial resolution between two adjacent FBG spectrums reduces due to the effects of side lobes.…”

Section: Introductionmentioning

confidence: 99%

Theoretical investigation of the impact of apodized fiber Bragg grating and machine learning approaches in quasi-distributed sensing

Mandal

Sidhishwari

2023

Meas. Sci. Technol.

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An apodized FBG is designed to investigate the impacts of side lobe elimination in the quasi-distributed sensing for the estimation of measurands (like temperature and strain) to assess the condition of any civil structures such as bridges. The adjacent FBG spectrums may overlap with each other because of the impacts of temperature and strain due to the presence of high range of side lobes in the quasi-distributed sensing network. Therefore, elimination of side lobe is highly necessary by introducing the method of apodization. The sensitivity of designed apodized FBG is estimated by analyzing the variations in the Bragg wavelength due to the impacts of temperature and strain. The Bragg wavelength changes depending on the measurands could affect the grating period and the grating index of the FBG. The period of the grating and the grating index of the FBG are simultaneously varied by the temperature and the strain. To measure the physical parameter effectively, it is very much essential to distinguish whether the changes in the Bragg wavelength are owing to the impacts of temperature or to the impacts of strain. The effect of cross-sensitivity between the temperature and the strain is a key problem in any FBG-based sensing applications as both the measurands can affect the Bragg wavelength. In this work, the Machine Learning (ML) methods (such as Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Decision Tree, and Ensemble model) are introduced to differentiate the effects of temperature and strain on a single Bragg wavelength shift measurement. The Artificial Neural Network (ANN) is used for the predictive analysis of physical parameters to realize any detrimental scenario of any measurands. It has been noted that the performance of the proposed Ensemble model is higher compared to other models for the classification of the temperature and the strain.

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Design of an Apodized Fiber Bragg Grating Sensor for Sensitivity Analysis of Physical Parameters using Support Vector Machine

Mandal,

Sidhishwari

2024

Braz J Phys

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Predictive Analysis on Apodized FBG for Quasi-Distributed Temperature-Strain Sensing

Cited by 6 publications

References 0 publications

Optimization and analysis of apodized fiber Bragg grating properties for quasi-distributed sensing

Optimization and analysis of apodized fiber Bragg grating properties for quasi-distributed sensing

Theoretical investigation of the impact of apodized fiber Bragg grating and machine learning approaches in quasi-distributed sensing

Design of an Apodized Fiber Bragg Grating Sensor for Sensitivity Analysis of Physical Parameters using Support Vector Machine

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